1 - Download from the net a grey level image in the field of visible, discover the resolution (n.
of pixels) , and compute the space necessary to store it, by assuming to code it in the pure
binary number code. Read the image, diplay it with a title, and compute the space
requirements by Matlab. Help: Feel confident and try the basic following Matlab built in
functions: input, imread, figure, title, xlabel, imshow, clear, sizeof.


2 - Write the code capable of reducing the number of intensity levels in a image from 256 to
2, in integer powers of 2. The desired number of intensity levels needs to be a variable
input to your program.


3 - Write the code to add Gaussian noise to an image. You must be able to specify the noise
mean and variance. Write the code to add salt-and-pepper (impulse) noise to an
image. You must be able to specify the probabilities of each of the two noise components.


4 - Write a program to compute the root-mean-square error of a compressed- decompressed
image.


5 - Download an image (if color image, perform a conversion to a gray level image) and write
the code which computes the entropy. Compare the computed value to the value provided
by the built-in matlab function entropy


6 - With reference to figure 7.1 of the book, write the code which extracts 4 blocks of 32 X 32
pixels of the image and compute and plot the histograms.


7 - Download a gray-level image (or convert a color image to a grey-level one) compute the
discrete cosine transform (block 8x8) and plot it at a proper scale.Threshold the values of
the coefficients of the cosine transform (the threshold is parametric) and perform the
inverse discrete cosine transform. Try several values of threshold (i.e., the mean value of
the coefficients, the maximum value/2, ...)


8 - Compute the root mean squared error between the original image of point 3 and the result
of step 4.


9 - With reference to figure 7.8 of the book, write the code to extract 4 bands of the wavelet
and compute the histogram.


10 - Write the code for computing the Haar Wavelet and use it to extract the horizontal edges
(hint: choose an image where horizontal edges are evident and predominant).


11 - Write a code for adding Gaussian noise to an image and use the Haar wavelet for denoising
it.


12 - Write the code for computing one of the Daubechies Wavelet (choose the one you like),
discard a certain amount of coefficients and perform the Inverse transform. Compute the
root mean squared error between the original image and the reconstructed one.
